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24-hour movement behaviours and mental health in non-clinical populations: A systematic review

The 24-hour movement guidelines consider movement behaviours (sleep, exercise, sedentary time) together within the frame of our 24-hour limit to provide recommendations on how a physically healthy day should look. There is increasing evidence that daily movement behaviours are associated with mental health. However the research into the relationship between 24-hour-movement and mental health, particularly in adults, is still to be systematically reviewed. The aim of this systematic review was to synthesise the current state of knowledge regarding movement behaviours and mental health in non-clinical child, adolescent and adult samples. systematic literature search of PubMed, Scopus and Embase was conducted in 2022, and updated in 2024. The review was preregistered (PROSPERO: CRD42022312717). Due to heterogeneity of methods and analyses, narrative synthesis of the results was employed. Of 103 eligible studies, one was a randomised controlled trial and the remainder were observational. In children 19/27 studies (70%) found at least one significant positive relationship between movement behaviour and mental health, in adolescents 38/41 (93%) and in adults 41/46 (89%). Certainty of evidence was low. More controlled studies are needed to make causal conclusions, but it is evident that the composition of movement behaviours is associated with mental health, and these associations may be differentially manifest in different age groups. This has implications for public health and mental health campaigns.

Aldo-keto reductase family 1 member C3 (AKR1C3) gene polymorphism (rs12529) is associated with breast cancer in Bangladeshi population: A case-control study and computational investigation

Breast cancer is defined as the unchecked growth of breast cells, with imbalances in prostaglandin and steroid hormone metabolism contributing to disease risk by altering prostaglandin types and forms (strong and weak) of steroid hormones. The AKR1C3 enzyme plays a key role in managing these metabolic processes. This study investigated the association between the AKR1C3 gene polymorphism (rs12529) and the risk of developing breast cancer in Bangladeshi individuals. A case-control investigation was conducted with a total of 620 samples, involving 310 individuals diagnosed with breast cancer and 310 healthy subjects. Herein, DNA extraction was performed via an organic process, whereas genotyping was employed via the PCR‒RFLP technique. Statistical assessments were conducted to analyze the association of polymorphisms, while molecular dynamics simulation and diverse computational techniques were employed to anticipate the functional and structural impacts of the SNP. Our study discovered that the rs12529 polymorphism of the AKR1C3 gene has an enhanced risk of susceptibility to breast malignancy (p = 0.016, OR = 1.97, 95% CI = 1.22 to 3.16 for the GG genotype in additive model 2). The recessive model (GG vs CC+CG) also showed an enhanced risk of susceptibility to breast malignancy (p = 0.0004, OR = 1.95, 95% CI =  1.40 to 2.73). In both premenopausal women and postmenopausal women, the GG genotype (for the recessive model) significantly increased breast cancer risk by 1.92-fold and 1.95-fold, respectively. However, no significant associations were observed regarding tumor grade or size in breast cancer development. In-silico analyses indicated that the H5Q (rs12529) mutation may decrease protein stability but is typically tolerated or functionally neutral. Molecular dynamics simulations revealed that H5Q leads to increased structural fluctuations and surface exposure, potentially causing the mutant AKR1C3 enzyme to operate differently from the wild type. In conclusion, rs12529 significantly increases the incidence of breast cancer in the population of Bangladesh. Computational analyses further revealed that the H5Q (rs12529) mutation in AKR1C3 leads to decreased stability and altered functional changes with notable conformational changes.

Ancient DNA integrates fossil and modern giant salamander taxonomy

Absence of item origin bias on a Brazilian interinstitutional Progress Test examination: A pooled analysis of items approach

Background It has been proposed that the school origin of items for cross-institutional Progress Tests (PTs) may introduce a bias in favour of students from the same school, posing a potential threat to the validity and reliability of PT results and cross-institutional comparisons. The aim of this study was to examine whether origin bias is present in a Brazilian cross-institutional PT examination. Methods This study conducted a cross-sectional analysis of seven schools affiliated with the oldest PT consortium in Brazil, utilising a pooled analysis of differences in students’ performance concerning self and non-self items. A proportional meta-analysis of the items’ rate differences and confidence intervals with random effects was performed, providing an odds ratio (OR) for self and non-self items. Differences between the two groups of items were assessed by scrutinising whether the OR and 95% confidence intervals overlapped. Results The findings indicated no discernible differences in psychometric indices based on the school responsible for item creation. Three schools consistently demonstrate superior performance on items authored by their faculty, however, these they also excelled on non-self items. Furthermore, an overlap in the 95% confidence intervals for both self and non-self items was observed across all seven schools. Conclusions In contrast to prior reports, this study revealed the absence of origin bias, suggesting that adoption of best practices in blueprinting, item writing, and editing may have played a role in mitigating such bias.

Diabetes rescue, engagement, and management (D-REM) for hypoglycemia: Clinical trial protocol of a community paramedic program to improve diabetes management among adults with severe hypoglycemia

Background Diabetes is among the most prevalent chronic conditions in the United States. Challenges in optimal diabetes care include fragmented care, gaps in diabetes self-management education, and high treatment burden. Severe hypoglycemia, a serious and potentially preventable event, indicates the need for treatment optimization. Inadequate or inaccessible care increases hypoglycemia risk. Community paramedics are well-positioned to fill these care gaps by providing focused diabetes self-management education and improving patient self-efficacy. Integrating community paramedics into care teams offers a novel pathway to improve diabetes outcomes. Methods and analysis We will conduct a pragmatic 2-group, parallel-arm, randomized clinical trial of a community paramedic–led “Diabetes Rescue, Engagement, and Management” program to enhance diabetes self-management in patients with a history of hypoglycemia. The study will enroll 150 adults (≥18 years) with diabetes and a history of level 3 hypoglycemia from 5 counties in Minnesota. Participants identified as having hypoglycemia (from an integrated health system and the primary ambulance service in the area) will be randomly assigned to the program intervention or to usual care. The intervention group will receive community paramedic home visits for approximately 1 month to deliver diabetes self-management education tailored to individual needs. Both groups will receive written diabetes education and resource materials. Outcomes include change in diabetes self-management, hypoglycemia, hyperglycemia, hemoglobin A1c level, diabetes distress, and health-related quality of life, assessed at baseline, 1 month, and 4 months. Qualitative interviews of 16 intervention participants and 16 persons who decline participation will be analyzed to understand the program’s effects and reasons for nonparticipation, to inform future program design. Trial registration ClinicalTrials.gov NCT04874532

Impact of mtG3PDH inhibitors on proliferation and metabolism of androgen receptor-negative prostate cancer cells: Role of extracellular pyruvate

Mitochondrial glycerol 3-P dehydrogenase (mtG3PDH) plays a significant role in cellular bioenergetics by serving as a rate-limiting element in the glycerophosphate shuttle, which connects cytosolic glycolysis to mitochondrial oxidative metabolism. mtG3PDH was identified as an important site of electron leakage leading to ROS production to the mitochondrial matrix and intermembrane space. Our research focused on the role of two published mtG3PDH inhibitors (RH02211 and iGP-1) on the proliferation and metabolism of PC-3 and DU145 prostate cancer cells characterized by different mtG3PDH activities. Since pyruvate as a substrate of lactate dehydrogenase (LDH) may represent an escape mechanism for the recycling of cytosolic NAD+ via the glycerophosphate shuttle, we investigated the effect of pyruvate on the mode of action of the mtG3PDH inhibitors. Extracellular pyruvate weakened the growth-inhibitory effects of RH02211 and iGP-1 in PC-3 cells but not in DU145 cells, which correlated with higher H-type LDH and lower mitochondrial glutamate-oxaloacetate transaminase in DU145 cells. In the pyruvate-low medium, the strength of inhibition was more pronounced in PC-3 cells, characterized by higher mtG3PDH activities compared to DU145 cells. Pyruvate conversion rates (production in pyruvate-low and consumption in pyruvate-high PC-3 cells) were not impaired by RH02211 and iGP-1, suggesting that the conversion of extracellular pyruvate to lactate was not the primary factor responsible for the weakening effect of extracellular pyruvate on the RH02211-induced inhibition of PC-3 proliferation. In pyruvate-high PC-3 cells, the intracellular glycerol-3-P and dihydroxyacetone-P concentrations were consistent with an inhibition of mtG3PDH. In contrast, in pyruvate-low cells, the concentrations of these metabolites suggested an activation of mtG3PDH in parallel with an impairment of cytosolic G3PDH by RH02211. Of all metabolic characterizations recorded in this study (fluxes, intracellular intermediates, O2 consumption and H2O2 production), the decrease in glutaminolysis correlated best with the RH02211-induced inhibition of proliferation in pyruvate-low and pyruvate-high PC-3 cells.

A qualitative exploration into the experience of mindfulness in moderate-severe persistent depression

Depression is a common and growing mental health problem, with around 5% of the world’s population experiencing an episode of depression during their lifetime. Relapse rates are high, with around half experiencing more than one depressive episode and a further 10–20% experiencing a chronic and persistent depression. Mindfulness has been incorporated into treatments for depression and several studies have explored the impact of mindfulness training on depressive symptomatology and recurrence. However, to date no studies have looked at the changing relationship between mindfulness and depression in those naïve to mindfulness training. 20 participants with moderate-to-severe persistent depression were interviewed to explore their experience of mindfulness in the context of low mood. Thematic analysis captured six themes highlighting changes in mindfulness relating to the onset of depression. Themes included: behavioural withdrawal; perceptual detachment from one’s experience; intentional reduction in awareness; increased self-criticism; mind racing; impaired cognitive performance. Thematic analysis suggested that mindfulness reduces in the context of moderate-to-severe persistent depression. This appears to occur indirectly as the consequence of depression-related processes, e.g., rumination and experiential avoidance, but also arises as a deliberately instigated self-protective strategy. However, findings seemed to indicate that reduced mindfulness maintains and intensifies depressive experience. Despite growing evidence of the value of mindfulness approaches for those with more chronic and severe depression, study findings suggest that introducing mindfulness to this population may be particularly challenging due to the intensity of symptomatology potentially obstructing access to a mindful perspective. Findings bear important implications for the treatment of depression and can inform future intervention development and delivery.

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks

Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images is important for stroke diagnosis, but most studies have focused on lesion segmentation using DWI. In this study, we compared the performance of lesion segmentation using DWI and ADC images. This study was conducted using a retrospective design A dataset was constructed using data from 360 patients with ischemic stroke collected from Gachon University Gil Medical Center. Artificial intelligence models, U-Net, and a fully connected network (FCN), were used to train each type of image data. The performance of the models was validated using five-fold cross-validation and evaluated based on metrics such as the dice similarity coefficient (DSC), accuracy, precision, and recall. As a result, the U-Net model demonstrated a DSC of 92.13 ± 0.91% on DWI and 83.68 ± 10% on ADC, whereas the FCN model exhibited a DSC of 82.86 ± 1.56% on DWI and 79.26 ± 1.19% on ADC. These metrics indicated that the trained models were suitable for lesion segmentation. A comparative analysis of DWI and ADC based on the trained models revealed similar results across the models, suggesting that lesion segmentation on ADC images is appropriate. For future research, the accuracy of ADC images is recommended to be imporved by utilizing images with different b-values, or training models with datasets that combe DWI and ADC images based on enhanced data.

Designed for simplicity, used for complexity: The systemic pressures shaping walk-in clinic practices and outcomes

Walk-in clinics (WICs), appreciated for their accessibility and convenience, have become an increasingly popular healthcare option in Ontario for patients with and without primary care enrolment. Despite their utility, WICs face criticism for delivering lower-quality care compared to comprehensive, enrolment-based primary care models. Critics argue that WICs contribute to system inefficiencies and encourage practice patterns misaligned with population health goals. This study explored physician perspectives on two key outcomes often associated with low-quality care in WICs: repeat primary care visits and potentially inappropriate antibiotic prescribing. Using a qualitative descriptive approach, semi-structured interviews were conducted with Ontario-based family physicians (N = 19) who had experience practicing in both WICs and enrolment-based primary care. The findings highlight systemic challenges, including limited access to enrolment-based primary care and increasing healthcare demands, which have pushed WICs beyond their intended role. This misalignment has created tensions between their structure and purpose, resulting in visits that participants described as more transactional than those in primary care. These constraints—rooted in a lack of informational and relational continuity—often limited participants’ ability to provide in-depth engagement or follow-up care. Repeat visits were frequently linked to efforts to ensure continuity for complex or chronic conditions. Similarly, participants acknowledged the reality of potentially inappropriate antibiotic prescribing, attributing it to the high patient volume, desire to satisfy patient expectations, and a tendency to “err on the side of caution” when the nature of the illness is in question. The findings underscore how health system pressures and well-intended policies, such as Ontario’s primary care access bonus, can produce unintended consequences, including inequities in access and difficulties with care coordination across settings. Addressing these challenges requires reforms to better integrate WICs with the primary care system, alongside tailored training to support physician decision-making in episodic care contexts.

The impact of wartime conflict on the mental health problems of women in the conflict-hit population in Woldia, Ethiopia

Background Common mental health problems are of significant public health importance, with severe social and economic impacts that adversely affect individuals’ quality of life. The burden of these problems may worsen during wartime. This study aims to assess the prevalence of war-related common mental health problems, including depression, anxiety disorder, phobia, and posttraumatic stress disorder (PTSD), among women in the Woldia district, Amhara, Ethiopia. Methods A community-based cross-sectional survey was conducted from February to March 2023, involving 1,505 eligible women selected from five kebeles using cluster followed by systematic sampling. The study used the Patient Health Questionnaire-9, Generalized Anxiety Disorder scale, and PTSD Checklist-5 to assess common mental health disorders. In addition to descriptive analysis, the study employed binary and multivariable analysis to evaluate sociodemographic correlates and the presence of comorbidity for each mental disorder. Result Almost half of the women exhibited symptoms of common mental health problems, with about 33% experiencing comorbidity of two or more disorders. Depressive symptoms were more prevalent among older, single women and those with spouses using khat, whereas higher wealth and strong social support were protective factors. Similar risk and protective patterns were observed for generalized anxiety disorder (GAD) and posttraumatic stress disorder (PTSD). Older age, single status, and moderate-income increased risk, while a higher wealth index and stronger social support provided some protection. Conclusion The findings from Woldia reveal a severe mental health crisis among women post-conflict, with elevated levels of depression, anxiety, and PTSD far exceeding global averages. This crisis jeopardizes the well-being of women and has far-reaching implications for families and communities, necessitating an urgent and multi-dimensional approach to address risk and protective factors identified in the study.

Comparison of WHO laboratory-based and non-laboratory-based CVD risk charts among hypertensive adults attending primary healthcare centers in West Africa sub-region

Background The World Health Organization (WHO) non-laboratory cardiovascular disease (CVD) risk chart is sub-region-specific and is advocated in resource-constrained settings. However, the extent of agreement with laboratory-based assessment among hypertensive adults attending primary health centers (PHCs) in the West Africa sub-region remains unknown. This study compared 10-year CVD risk among adults with hypertension attending PHCs in Ghana and Nigeria. Materials and methods This cross-sectional study recruited 319 adults with hypertension at PHCs in Ghana and Nigeria. All participants had their blood pressure, anthropometrics, fasting blood sugar, and fasting cholesterol measured following standard procedures. WHO laboratory and non-laboratory CVD risks were assessed and compared using Kappa statistics, correlation, and Bland-Altman Plot, Results The median (interquartile range) for laboratory-based and non-laboratory-based CVD risk scores were comparable [7.0 (4.0 11.0) vs. 7.0 (4.0 to 11.0), p = 0.914]. Of the 319 participants, laboratory-based assessment classified 214 (67.1%) as low risk, while 210 (65.8%) were classified as low risk using the non-laboratory method. Eleven (3.4%) and 14 (4.4%) participants were classified as high-risk using laboratory- and non-laboratory-based methods, respectively. Overall, there was a very good positive correlation between the CVD risk assessment methods (r = 0.948, p<0.001). For all participants combined, there was substantial agreement (Kappa statistics), with K = 0.766. Bland-Altman showed a mean bias of 0.15 (SD = 1.74) in favor of non-laboratory-based assessment of CVD with an upper limit of 3.57 and a lower limit of –3.26. Conclusion There was substantial agreement between laboratory- and non-laboratory-based WHO CVD risk charts in this study. In low-resource settings, such as Ghana and Nigeria, the WHO non-laboratory CVD risk prediction model offers a huge opportunity for primary CVD prevention in adults with hypertension.

Software technical debt prediction based on complex software networks

Technical debt prediction (TDP) is crucial for the long-term maintainability of software. In the literature, many machine-learning based TDP models have been proposed; they used TD-related metrics as input features for machine-learning classifiers to build TDP models. However, their performance is unsatisfactory. Developing and utilizing more effective metrics to build TDP models is considered as a promising approach to enhance the performance of TDP models. Social Network Analysis (SNA) uses a set of metrics (i.e., SNA metrics) to characterize software elements (classes, binaries, etc.) in software from the perspective of software as a whole. SNA metrics are regarded as a compensation of TD-related metrics used in the existing TDP work, and thus are expected to improve the performance of existing TDP models. However, the effectiveness of SNA metrics in the field of TDP has never been explored so far. To fill this gap, in this paper, we propose an improved software technical debt prediction approach. First, we represent software as a Class Dependency Network, based on which we compute the value of a set of SNA metrics. Second, we combine SNA metrics with the TD-related metrics to create a combined metric suite (CMS). Third, we employ CMS as the input features and utilize seven commonly used machine learning classifiers to build TDP models. Empirical results on a publicly available data set show that (i) the combined metric suite (i.e., CMS) can indeed improve the performance of existing TDP models; (ii) XGBoost performs best among the seven classifiers, with an F2 value of 0.77, an MI ratio of approximately 0.10, and a recall close to 0.87. Furthermore, we also reveal the relative effectiveness of different metric combinations.

The effect of laboratory critical value reporting on patient management at Siriraj Hospital – Thailand’s largest national tertiary referral center

Critical laboratory values are life-threatening results that necessitate immediate medical intervention. Reporting these values according to established guidelines is essential for ensuring optimal patient safety and care quality. The aim of this study was to evaluate the laboratory critical value reporting system and the actions taken at Siriraj Hospital – Thailand’s oldest and largest teaching hospital – during January 2018. This study reviewed critical values from hematology, coagulation, and clinical chemistry tests over a one-month period. Patient management actions in response to critical values were classified into five categories: treatment, further investigation, monitoring, treatment combined with investigation, and other. Descriptive statistics were used to analyze the data in Microsoft Excel 2019, calculating the incidence of critical values, notification rates, and management actions. Of the 253,537 tests that were performed, 2,722 critical levels were found, indicating an incidence rate of 1.1%. Hemoglobin and potassium were the most frequently observed critical parameters, accounting for 25.61% and 23.99% of cases, respectively. The rate of notification varied depending on the specific parameter and patient category. For critical glucose and potassium levels, the most common response was close monitoring within 30 minutes, followed by treatment in 80% of cases. Hypermagnesemia, a condition linked to preeclampsia and treated with magnesium sulfate, required particularly careful monitoring. The 1.1% incidence of critical values in this study is high compared to previously published international data; however, this may be explained by the high volume of complex cases referred to our national tertiary referral center. Critical value reporting criteria should be established based on patient conditions and hospital management practices to reduce unnecessary alerts, optimize laboratory workload, and ensure high-quality patient care.

SHIP-AGE: Frailty, renal function, and multi-component primary care in rural Mecklenburg-West Pomerania (MV-FIT)- study protocol

Background. Chronic kidney disease (CKD) is a leading risk factor for cardiovascular disease and all-cause mortality among older adults. Mecklenburg-West Pomerania has the highest CKD prevalence in Germany and Europe, however, its impact on frailty prevention strategies in primary care remains poorly understood. The SHIP-AGE/MV-FIT study aims to investigate the role of CKD in frailty incidence. Methods. The SHIP-AGE/MV-FIT cohort is a prospective, longitudinal, population-based observational study targeting individuals ≥ 65 years with mGFR >30 mL/min. The cohort will consist of approximately 820 elderly participants who will be monitored over a three-year period. They will undergo a comprehensive, multi-factorial geriatric assessment, along with a structured monitoring and management program aimed at preventing frailty. The program incorporates evidence-based, multi-component care, including physical activity, medication review, nutritional optimization, and fall prevention strategies. Discussion. SHIP-AGE/MV-FIT will clarify CKD’s role in frailty progression and identify mechanisms underlying frailty and pre-frailty. Additionally, the study aims to implement and evaluate multi-component healthcare strategies for frailty and fall prevention, assess patient adherence and quality of life, and explore elderly individuals’ experiences with primary care interventions. By integrating SHIP-AGE data with findings from the SHIP (Study of Health in Pomerania) cohorts in our region, this research will contribute to evidence-based strategies for maintaining health, independence, and well-being in aging populations, particularly in rural primary care settings, such as Mecklenburg-West Pomerania.

BanglaNewsClassifier: A machine learning approach for news classification in Bangla Newspapers using hybrid stacking classifiers

Bangla news floods the web, and the need for smarter and more efficient classification techniques is greater than ever. Previous studies mostly focused on traditional models, overlooking the potential of hybrid techniques to handle the ever-growing complex dataset and its linguistic patterns in Bangla to achieve higher accuracy. Addressing the challenge, this study presents a comprehensive approach to classify Bangla news articles into eight distinct categories using various machine learning and deep learning techniques. The use of traditional machine learning algorithms, deep learning architectures, and hybrid models, including novel stacking classifiers, was a part of our experiment. This study utilized a dataset of 118,404 Bangla news articles, applying rigorous feature extraction techniques including TF-IDF vectorization and word2Vec embeddings. Our best-performing model, a stacking meta-classifier combining bidirectional long short-term memory and support vector machine, achieved a remarkable 94% accuracy, leaving all basic models’ performance behind. Also, we provided an in-depth analysis of model performances, including confusion matrices, ROC curves, and error analysis, offering insights into the strengths and limitations of each approach. This research contributes significantly to the field of Bangla natural language processing and demonstrates the efficacy of ensemble methods and deep learning in news classification for low-resource languages.

Unraveling job demand-control-support patterns and job stressors as predictors: Cross-sectional latent profile and network analysis among Italian hospital workers

The Job Demand-Control-Support (JDCS) model postulates that patterns of job demand, job control, and social support lead to eight job types that are associated with well-being and health. This study employed latent profile analysis (LPA) to identify JDCS profiles among Italian hospital workers (n = 1464) and examined the predictive roles of role clarity and negative relationships at work on profile membership. Furthermore, adopting a network perspective, this study explored the interrelationships among JDCS factors within each identified profile. The LPA results revealed four profiles: isolated prisoner, moderate strain, low strain, and participatory leader. In addition, role clarity increased the likelihood of being included in the low-strain, moderate-strain, and participatory leader profiles. In contrast, negative relationships at work increased the risk of being included in the isolated prisoner profile. Finally, the results of network analysis revealed that networks differed across profiles in terms of density (interconnections between nodes) and edge strength (magnitude of relationships between nodes). Our study extends previous JDCS research by highlighting that researchers should consider empirically identified profiles rather than theoretically defined subgroups. The implications for stress theory, future research, and practice are also discussed.

Investigation of locomotive syndrome improvement by total hip arthroplasty in patients with hip osteoarthritis: A before-after comparative study focusing on 25-question geriatric locomotive function scale

Background The 25-Question Geriatric Locomotive Function Scale (GLFS-25) is one of the tests used to assess the risk of locomotive syndrome (LS). It is a comprehensive tool for measuring LS improvement after total hip arthroplasty (THA) and provides beneficial information for rehabilitation after THA. The primary objective of this study was to clarify LS improvement in patients with hip osteoarthritis (OA) who have undergone unilateral primary THA using GLFS-25. A secondary objective was to identify the impact of THA on each specific GLFS-25 item for optimizing functional recovery. Methods The participants of this study were 273 patients who underwent primary THA for hip OA. LS was evaluated using the GLFS-25, stand-up test, and two-step test before receiving THA and three months after THA. Results Before THA, items rated as “moderately difficult” (score ≥2) in GLFS-25 included pain-related Q3 and Q4, activities of daily living (ADL)-related Q12, Q13, Q15, and Q18, and social function-related Q21 and Q23. At three months after THA, these subjective symptoms showed significant improvement. Further analysis of the relationship between these subjective symptom improvements and LS improvement revealed that all items, except pain-related Q3, were significantly associated with LS improvement. Conclusions Patients experienced not only severe hip pain and physical discomfort but also significant difficulties with activities of daily living (ADL) and social participation before THA. LS improvement after THA was strongly associated with improvements in the subjective symptoms of ADL and social functioning. Based on these findings, rehabilitation strategies that focus on enhancing mobility, improving ADL and social engagement, and optimizing gait function after THA are crucial for further supporting LS recovery.

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

A web-based workplace exercise intervention among office workers with spinal pain: Protocol of a mixed methods study

Introduction Musculoskeletal disorders are a major cause of disability worldwide, significantly impacting office workers due to prolonged sitting and lack of movement. Implementing therapeutic exercise interventions in the workplace has been identified as a feasible and cost-effective strategy to address spinal pain. However, understanding workers’ perspectives and workplace barriers is essential for designing effective interventions. This study aims to develop and evaluate a web-based workplace intervention with active breaks to reduce spinal pain among office workers. Methods This study follows a sequential exploratory mixed-methods design. The qualitative phase will use semi-structured interviews with office workers to explore their experiences with spinal pain, active breaks, and perceived barriers to implementation. These findings will inform the development of a six-week web-based therapeutic exercise intervention, which will be evaluated through a two-arm cluster randomised controlled trial. The trial will compare an intervention group performing structured active breaks during work hours with a control group maintaining their usual routine. Primary outcomes include pain intensity (Visual Analogue Scale), spinal dysfunction (Spine Functional Index), and adherence to the program. Secondary outcomes include quality of life (EQ-5D-5L) and exercise motivation (Behavioural Regulation in Exercise Questionnaire-2). Statistical analyses will compare within- and between-group differences to assess the intervention’s effectiveness. Discussion Web-based interventions can enhance adherence to active breaks and provide an accessible, cost-effective solution for spinal pain management in sedentary workplaces. By adopting a mixed-methods approach, this study will generate valuable insights into implementing workplace exercise interventions, taking into account workers’ expectations, workplace context, and adherence factors. Findings may inform future interventions aimed at managing musculoskeletal disorders in office workers. Trial registration ClinicalTrials.gov NCT05571124

Gestational weight gain and its determinants among pregnant women attending antenatal care at West Shawa Hospitals, Oromia, Ethiopia

Background Gestational Weight Gain (GWG) is a crucial factor influencing maternal and neonatal health outcomes. Identifying the determinants of GWG can help develop targeted interventions to improve pregnancy outcomes. Objective This study aimed to assess the magnitude of gestational weight gain and identify its determinants among pregnant women attending antenatal care (ANC) services at West Shoa Hospital, Ethiopia, in 2024. Methodology A bidirectional cohort study was conducted among 885 pregnant women attending antenatal care (ANC) services at West Shoa Hospitals, Ethiopia, before 12 weeks of gestation. Data were collected through face-to-face interviews using the CesPro application and document review. The determinants of GWG were analyzed using an ordinal logistic regression model, assuming the proportional odd assumptions. The Brant test was used to determine whether the parallel assumption was held. The STATA “ologit” command was used for ordinal regression, and the “brant” test was applied to verify the validity of the model. Odds ratios (OR) with 95% confidence intervals (CI) were estimated, and statistical significance was declared at p < 0.05. Results Approximately 69% of pregnant women experienced insufficient weight gain, 26% had adequate weight gain, and 5% had excessive weight gain during pregnancy. Pre-pregnancy Body Mass Index was a significant determinant of gestational weight gain. Compared to underweight women, overweight women had 10.58 times higher odds (95% CI: 5.24–21.37) of being in a higher weight gain category, while obese women had 10.64 times higher odds (95% CI: 1.87–60.57) of achieving normal or excessive gestational weight gain. Partner education significantly influenced gestational weight gain, with those who could only read and write having 0.22 times lower odds (95% CI: 0.05–0.98) of excessive weight gain compared to those with higher education. Maternal occupation also played a role, as daily laborers had 0.26 times lower odds (95% CI: 0.08–0.87) of adequate weight gain than employed women. The normal hemoglobin category was associated with increased odds of being in a higher weight gain category (adequate or excessive) compared to a lower category, with an odds ratio (OR) of 1.04 (95% CI: 1.01–1.08). Conversely, alcohol consumption was associated with lower odds of being in a higher weight gain category, with an OR of 0.49 (95% CI: 0.25–0.99), suggesting that alcohol drinkers had lower odds of experiencing normal or excessive weight gain compared to non-drinkers. Conclusion A significant proportion of pregnant women experienced inadequate gestational weight gain. Pre-pregnancy BMI, partner’s educational status, maternal occupation, hemoglobin levels, and alcohol consumption were key determinants of gestational weight gain. These findings highlight the need for targeted nutritional counseling and lifestyle interventions to promote optimal weight gain during pregnancy.